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Entrepreneurship and the taste for discrimination

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Abstract

This paper analyzes the connection between discrimination and entrepreneurship. We contend that the entrepreneur is the central mechanism through which inefficiencies associated with discrimination are competed away. In addition to illuminating the mechanism through which existing discrimination tends to be eliminated, we also consider the more difficult case of consumer discrimination. The standard assumption is that consumer discrimination will not be competed away through market forces. In contrast, we find that entrepreneurs can correct the inefficiencies associated with this form of discrimination by influencing the costs and benefits associated with consumer discrimination. We empirically analyze the integration of black players in Major League Baseball to illustrate our theoretical arguments regarding entrepreneurship and consumer discrimination.

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Notes

  1. For a survey of the discrimination literature, see Cain (1986).

  2. It should be noted that in addition to Becker’s taste-based theory of discrimination, there is also a literature on the statistical theory of discrimination (see Phelps 1972; Arrow 1973). The statistical theory of discrimination focuses on the information asymmetries facing firms. Because firms have limited information, they tend to intuit the expected productivity of job applicants based on observable characteristics. Our focus in this paper is on the taste-based theory of discrimination.

  3. Becker is careful to show that the erosion of discrimination depends not only on the taste for discrimination but also on firms production functions (1957: 43–45). There is also a literature exploring the existence and persistence of discrimination under various industry structures. See for instance Doeringer and Piore (1971) and Black (1995).

  4. We would like to thank an anonymous referee for suggesting this addition.

  5. We use ‘quality’ as a general term to define any non-price variable that may appear in a consumer’s utility function.

  6. See Srinagesh and Bradburd (1989) who demonstrate the appropriate reference for the location of quality difference depends on the consumer groups. See also Mussa and Rosen (1978) and Ecchia and Lambertini (1997) for the role of utility theory and quality differences among products.

  7. Such a result may help to explain why cities with multiple baseball teams only had one team integrate during the early years. Under a duopoly scenario, such an outcome ensures full market coverage (Mussa and Rosen 1978; Ecchia and Lambertini 1997), and a slower integration process.

  8. Olson and Schwab (2002) analyze the impact of the farm team system on the performance of its early adopters.

  9. Unlike our theoretical model where, discrimination was seen as a continuous interval between a and b, empirically we recognize that a team either integrates or it does not integrate, hence we use a non-continuous measure following Besanko et al. (1990).

  10. We have not included price in Eq. 2 since average ticket prices were stable over the period of integration. See www.rodneyfort.com for details. Further, ticket prices were set for any given year so that prices could not be changed during the season to reflect changes in relative quality as it was revealed.

  11. Productivity variables such as slugging average and earned run average may also help to predict whether a team is in pennant contention. Similar to Goff et al. (2002), we find that the number of black players positively affects slugging average; we further find that adding black players improves (lowers) earned run average even when only non-pitchers are added. Thus, since such productivity measures appear to be captured in the black variable, we have not included them here so as to avoid multicollinearity.

  12. There are a few candidates for regression type for Eq. 3, including the Poisson and the negative binomial. While the Poisson regression is commonly-used, it has the limiting restriction of the expected value of the mean equaling that of the variance. Cameron and Trivedi (1986) argue that the Poisson regression model may yield results that overestimate statistical significance when data are overdispersed and this limiting assumption does not hold. In cases where this limiting restriction is too limiting, the negative binomial regression model relaxes this restriction. Moreover, as Long (1996) points out, the negative binomial regression model addresses the issue of underprediction of zeros in the Poisson regression model.

  13. Since we model CP solely as a function of BLACK—and the coefficient is marginally significant—we also estimated Eq. 4 with a random effects specification as a robustness check. The random effects coefficient is 0.3643, with a z-statistic of 2.07. Further, we ran a Hausman (1978) test (χ 2 = 0.10 with 1 degree of freedom) that provides evidence that the random effects are consistent. Nevertheless, while the random effects provides statistical robustness, since we use the entire population, and Eq. 4 is purely instrumental, we have chosen to use the fixed effects results in our estimation of Eq. 2 (Greene 1997). We would like to thank an anonymous referee for making this suggestion.

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Correspondence to Christopher J. Coyne.

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Coyne, C.J., Isaacs, J.P. & Schwartz, J.T. Entrepreneurship and the taste for discrimination. J Evol Econ 20, 609–627 (2010). https://doi.org/10.1007/s00191-009-0164-6

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